{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean reverting strategy based on Bollinger bands Strategy\n",
    "\n",
    "This notebook answers question 3.5 form the text book Advances in Financial Machine Learning.\n",
    "\n",
    "3.5 Develop a mean-reverting strategy based on Bollinger bands. For each observation, the model suggests a side, but not a size of the bet.\n",
    "\n",
    "* (a) Derive meta-labels for ptSl = [0, 2] and t1 where numDays = 1. Use as trgt the daily standard deviation as computed by Snippet 3.1.\n",
    "* (b) Train a random forest to decide whether to trade or not. Use as features: volatility, seial correlation, and teh crossinmg moving averages.\n",
    "* (c) What is teh accuracy of prediction from the primary model? (i.e. if the secondary model does not filter the bets) What are the precision, recall and FI-scores?\n",
    "* (d) What is teh accuracy of prediction from the primary model? What are the precision, recall and FI-scores?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Hudson and Thames MlFinLab package\n",
    "import mlfinlab as ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/pyfolio/pos.py:28: UserWarning: Module \"zipline.assets\" not found; mutltipliers will not be applied to position notionals.\n",
      "  ' to position notionals.'\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyfolio as pf\n",
    "import timeit\n",
    "\n",
    "from sklearn.utils import resample\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in Data\n",
    "We are using the dollar bars based off of the high quality HFT data we purchased. There is a sample of bars available in this branch as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "data = pd.read_csv('official_data/dollar_bars.csv')\n",
    "data.index = pd.to_datetime(data['date_time'])\n",
    "data = data.drop('date_time', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Define helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute RSI\n",
    "def relative_strength_index(df, n):\n",
    "        \"\"\"Calculate Relative Strength Index(RSI) for given data.\n",
    "        https://github.com/Crypto-toolbox/pandas-technical-indicators/blob/master/technical_indicators.py\n",
    "        \n",
    "        :param df: pandas.DataFrame\n",
    "        :param n: \n",
    "        :return: pandas.DataFrame\n",
    "        \"\"\"\n",
    "        i = 0\n",
    "        UpI = [0]\n",
    "        DoI = [0]\n",
    "        while i + 1 <= df.index[-1]:\n",
    "            UpMove = df.loc[i + 1, 'high'] - df.loc[i, 'high']\n",
    "            DoMove = df.loc[i, 'low'] - df.loc[i + 1, 'low']\n",
    "            if UpMove > DoMove and UpMove > 0:\n",
    "                UpD = UpMove\n",
    "            else:\n",
    "                UpD = 0\n",
    "            UpI.append(UpD)\n",
    "            if DoMove > UpMove and DoMove > 0:\n",
    "                DoD = DoMove\n",
    "            else:\n",
    "                DoD = 0\n",
    "            DoI.append(DoD)\n",
    "            i = i + 1\n",
    "        UpI = pd.Series(UpI)\n",
    "        DoI = pd.Series(DoI)\n",
    "        PosDI = pd.Series(UpI.ewm(span=n, min_periods=n).mean())\n",
    "        NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean())\n",
    "        RSI = pd.Series(round(PosDI * 100. / (PosDI + NegDI)), name='RSI_' + str(n))\n",
    "        # df = df.join(RSI)\n",
    "        return RSI\n",
    "\n",
    "def get_rsi(data, window=14):\n",
    "    df = data.copy(deep=True).reset_index()\n",
    "    rsi = relative_strength_index(df, window)\n",
    "    rsi_df = pd.Series(data=rsi.values, index=data.index)\n",
    "    return rsi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bbands(close_prices, window, no_of_stdev):\n",
    "    # rolling_mean = close_prices.rolling(window=window).mean()\n",
    "    # rolling_std = close_prices.rolling(window=window).std()\n",
    "    rolling_mean = close_prices.ewm(span=window).mean()\n",
    "    rolling_std = close_prices.ewm(span=window).std()\n",
    "\n",
    "    upper_band = rolling_mean + (rolling_std * no_of_stdev)\n",
    "    lower_band = rolling_mean - (rolling_std * no_of_stdev)\n",
    "\n",
    "    return rolling_mean, upper_band, lower_band"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Fit a Primary Model: Mean-reverting based on Bollinger bands\n",
    "Based on the mean-reverting Bollinger band strategy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>cum_vol</th>\n",
       "      <th>cum_dollar</th>\n",
       "      <th>cum_ticks</th>\n",
       "      <th>avg</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-14 18:42:20.172</th>\n",
       "      <td>2000.75</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>1997.00</td>\n",
       "      <td>1997.75</td>\n",
       "      <td>34996</td>\n",
       "      <td>70009302.50</td>\n",
       "      <td>4853</td>\n",
       "      <td>2001.812021</td>\n",
       "      <td>2018.207702</td>\n",
       "      <td>1985.416341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-29 08:25:44.953</th>\n",
       "      <td>2166.75</td>\n",
       "      <td>2168.50</td>\n",
       "      <td>2164.75</td>\n",
       "      <td>2166.00</td>\n",
       "      <td>32341</td>\n",
       "      <td>70059966.00</td>\n",
       "      <td>5242</td>\n",
       "      <td>2167.345340</td>\n",
       "      <td>2175.728175</td>\n",
       "      <td>2158.962505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-19 15:18:35.973</th>\n",
       "      <td>2008.25</td>\n",
       "      <td>2008.75</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>2008.00</td>\n",
       "      <td>34862</td>\n",
       "      <td>70001493.50</td>\n",
       "      <td>8509</td>\n",
       "      <td>2005.388024</td>\n",
       "      <td>2014.141033</td>\n",
       "      <td>1996.635016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-26 08:44:18.011</th>\n",
       "      <td>1400.00</td>\n",
       "      <td>1402.00</td>\n",
       "      <td>1398.50</td>\n",
       "      <td>1401.00</td>\n",
       "      <td>49977</td>\n",
       "      <td>70001943.25</td>\n",
       "      <td>17390</td>\n",
       "      <td>1392.233600</td>\n",
       "      <td>1406.956662</td>\n",
       "      <td>1377.510539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-06 10:00:53.729</th>\n",
       "      <td>1388.00</td>\n",
       "      <td>1392.25</td>\n",
       "      <td>1387.25</td>\n",
       "      <td>1391.50</td>\n",
       "      <td>50357</td>\n",
       "      <td>70011252.50</td>\n",
       "      <td>17824</td>\n",
       "      <td>1380.869496</td>\n",
       "      <td>1397.779918</td>\n",
       "      <td>1363.959073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-15 14:14:46.821</th>\n",
       "      <td>1647.50</td>\n",
       "      <td>1648.00</td>\n",
       "      <td>1646.00</td>\n",
       "      <td>1646.25</td>\n",
       "      <td>42519</td>\n",
       "      <td>70032737.25</td>\n",
       "      <td>9276</td>\n",
       "      <td>1643.182281</td>\n",
       "      <td>1652.023239</td>\n",
       "      <td>1634.341324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-23 15:35:45.997</th>\n",
       "      <td>1401.00</td>\n",
       "      <td>1402.25</td>\n",
       "      <td>1400.75</td>\n",
       "      <td>1400.75</td>\n",
       "      <td>49972</td>\n",
       "      <td>70031433.25</td>\n",
       "      <td>11601</td>\n",
       "      <td>1387.832101</td>\n",
       "      <td>1400.390427</td>\n",
       "      <td>1375.273774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-07 16:55:01.597</th>\n",
       "      <td>1874.00</td>\n",
       "      <td>1876.00</td>\n",
       "      <td>1873.50</td>\n",
       "      <td>1873.50</td>\n",
       "      <td>37346</td>\n",
       "      <td>70020386.75</td>\n",
       "      <td>10809</td>\n",
       "      <td>1876.440819</td>\n",
       "      <td>1882.934840</td>\n",
       "      <td>1869.946798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-16 17:32:22.426</th>\n",
       "      <td>1181.75</td>\n",
       "      <td>1184.75</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1184.25</td>\n",
       "      <td>59326</td>\n",
       "      <td>70150933.00</td>\n",
       "      <td>18625</td>\n",
       "      <td>1189.130622</td>\n",
       "      <td>1200.548842</td>\n",
       "      <td>1177.712403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 15:19:00.008</th>\n",
       "      <td>2903.00</td>\n",
       "      <td>2903.00</td>\n",
       "      <td>2900.75</td>\n",
       "      <td>2902.00</td>\n",
       "      <td>24142</td>\n",
       "      <td>70056605.00</td>\n",
       "      <td>2974</td>\n",
       "      <td>2903.062929</td>\n",
       "      <td>2909.515651</td>\n",
       "      <td>2896.610208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            open     high      low    close  cum_vol  \\\n",
       "date_time                                                              \n",
       "2015-12-14 18:42:20.172  2000.75  2003.00  1997.00  1997.75    34996   \n",
       "2016-08-29 08:25:44.953  2166.75  2168.50  2164.75  2166.00    32341   \n",
       "2014-09-19 15:18:35.973  2008.25  2008.75  2007.00  2008.00    34862   \n",
       "2012-11-26 08:44:18.011  1400.00  1402.00  1398.50  1401.00    49977   \n",
       "2012-08-06 10:00:53.729  1388.00  1392.25  1387.25  1391.50    50357   \n",
       "2013-05-15 14:14:46.821  1647.50  1648.00  1646.00  1646.25    42519   \n",
       "2012-11-23 15:35:45.997  1401.00  1402.25  1400.75  1400.75    49972   \n",
       "2014-03-07 16:55:01.597  1874.00  1876.00  1873.50  1873.50    37346   \n",
       "2011-08-16 17:32:22.426  1181.75  1184.75  1180.00  1184.25    59326   \n",
       "2018-08-31 15:19:00.008  2903.00  2903.00  2900.75  2902.00    24142   \n",
       "\n",
       "                          cum_dollar  cum_ticks          avg        upper  \\\n",
       "date_time                                                                   \n",
       "2015-12-14 18:42:20.172  70009302.50       4853  2001.812021  2018.207702   \n",
       "2016-08-29 08:25:44.953  70059966.00       5242  2167.345340  2175.728175   \n",
       "2014-09-19 15:18:35.973  70001493.50       8509  2005.388024  2014.141033   \n",
       "2012-11-26 08:44:18.011  70001943.25      17390  1392.233600  1406.956662   \n",
       "2012-08-06 10:00:53.729  70011252.50      17824  1380.869496  1397.779918   \n",
       "2013-05-15 14:14:46.821  70032737.25       9276  1643.182281  1652.023239   \n",
       "2012-11-23 15:35:45.997  70031433.25      11601  1387.832101  1400.390427   \n",
       "2014-03-07 16:55:01.597  70020386.75      10809  1876.440819  1882.934840   \n",
       "2011-08-16 17:32:22.426  70150933.00      18625  1189.130622  1200.548842   \n",
       "2018-08-31 15:19:00.008  70056605.00       2974  2903.062929  2909.515651   \n",
       "\n",
       "                               lower  \n",
       "date_time                             \n",
       "2015-12-14 18:42:20.172  1985.416341  \n",
       "2016-08-29 08:25:44.953  2158.962505  \n",
       "2014-09-19 15:18:35.973  1996.635016  \n",
       "2012-11-26 08:44:18.011  1377.510539  \n",
       "2012-08-06 10:00:53.729  1363.959073  \n",
       "2013-05-15 14:14:46.821  1634.341324  \n",
       "2012-11-23 15:35:45.997  1375.273774  \n",
       "2014-03-07 16:55:01.597  1869.946798  \n",
       "2011-08-16 17:32:22.426  1177.712403  \n",
       "2018-08-31 15:19:00.008  2896.610208  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute bands\n",
    "window = 50\n",
    "data['avg'], data['upper'], data['lower'] = bbands(data['close'], window, no_of_stdev=1.5)\n",
    "data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute RSI\n",
    "rsi_df = get_rsi(data, window=14)\n",
    "data['rsi'] = pd.Series(data=rsi_df.values, index=data.index)\n",
    "\n",
    "# Drop the NaN values from our data set\n",
    "data.dropna(axis=0, how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Fit a Primary Model: Bollinger Band Mean-Reversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1.0    4242\n",
      "-1.0    3989\n",
      "Name: side, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Compute sides\n",
    "data['side'] = np.nan \n",
    "\n",
    "long_signals = (data['close'] <= data['lower']) \n",
    "short_signals = (data['close'] >= data['upper']) \n",
    "\n",
    "data.loc[long_signals, 'side'] = 1\n",
    "data.loc[short_signals, 'side'] = -1\n",
    "\n",
    "print(data.side.value_counts())\n",
    "\n",
    "# Remove Look ahead biase by lagging the signal\n",
    "data['side'] = data['side'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the raw data\n",
    "raw_data = data.copy()\n",
    "\n",
    "# Drop the NaN values from our data set\n",
    "data.dropna(axis=0, how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1.0    4242\n",
      "-1.0    3989\n",
      "Name: side, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.side.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter Events: CUSUM Filter\n",
    "Predict what will happen when a CUSUM event is triggered. Use the signal from the MAvg Strategy to determine the side of the bet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute daily volatility\n",
    "daily_vol = ml.util.get_daily_vol(close=data['close'], lookback=50)\n",
    "\n",
    "# Apply Symmetric CUSUM Filter and get timestamps for events\n",
    "# Note: Only the CUSUM filter needs a point estimate for volatility\n",
    "cusum_events = ml.filters.cusum_filter(data['close'], threshold=daily_vol['2011-09-01':'2018-01-01'].mean() * 0.1)\n",
    "\n",
    "# Compute vertical barrier\n",
    "vertical_barriers = ml.labeling.add_vertical_barrier(t_events=cusum_events, close=data['close'], num_days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/mlfinlab/labeling/labeling.py:117: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  target = target.loc[t_events]\n",
      "2019-04-18 16:32:37.463475 100.0% apply_pt_sl_on_t1 done after 0.09 minutes. Remaining 0.0 minutes.\n"
     ]
    }
   ],
   "source": [
    "pt_sl = [0, 2]\n",
    "min_ret = 0.0005\n",
    "triple_barrier_events = ml.labeling.get_events(close=data['close'],\n",
    "                                               t_events=cusum_events,\n",
    "                                               pt_sl=pt_sl,\n",
    "                                               target=daily_vol,\n",
    "                                               min_ret=min_ret,\n",
    "                                               num_threads=2,\n",
    "                                               vertical_barrier_times=vertical_barriers,\n",
    "                                               side_prediction=data['side'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    2558\n",
       "-1.0    2084\n",
       "Name: side, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ml.labeling.get_bins(triple_barrier_events, data['close'])\n",
    "labels.side.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Results of Primary Model:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      3785\n",
      "           1       0.18      1.00      0.31       857\n",
      "\n",
      "   micro avg       0.18      0.18      0.18      4642\n",
      "   macro avg       0.09      0.50      0.16      4642\n",
      "weighted avg       0.03      0.18      0.06      4642\n",
      "\n",
      "Confusion Matrix\n",
      "[[   0 3785]\n",
      " [   0  857]]\n",
      "\n",
      "Accuracy\n",
      "0.1846186988367083\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "primary_forecast = pd.DataFrame(labels['bin'])\n",
    "primary_forecast['pred'] = 1\n",
    "primary_forecast.columns = ['actual', 'pred']\n",
    "\n",
    "# Performance Metrics\n",
    "actual = primary_forecast['actual']\n",
    "pred = primary_forecast['pred']\n",
    "print(classification_report(y_true=actual, y_pred=pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(actual, pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(actual, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A few takeaways**\n",
    "* There is an imbalance in the classes - far more are classified as \"no trade\"\n",
    "* Meta-labeling says that there are many false-positives  \n",
    "* the sklearn's confusion matrix is [[TN, FP][FN, TP]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Fit a Meta Model\n",
    "Train a random forest to decide whether to trade or not (i.e 1 or 0 respectively) since the earlier model has decided the side (-1 or 1)\n",
    "\n",
    "Create the following features: \n",
    "* Volatility\n",
    "* Serial Correlation\n",
    "* The returns at the different lags from the serial correlation\n",
    "* The sides from the SMavg Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>cum_vol</th>\n",
       "      <th>cum_dollar</th>\n",
       "      <th>cum_ticks</th>\n",
       "      <th>avg</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "      <th>rsi</th>\n",
       "      <th>side</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08-01 13:56:56.220</th>\n",
       "      <td>1293.25</td>\n",
       "      <td>1294.00</td>\n",
       "      <td>1292.00</td>\n",
       "      <td>1292.25</td>\n",
       "      <td>54159</td>\n",
       "      <td>70025279.50</td>\n",
       "      <td>12010</td>\n",
       "      <td>1300.315764</td>\n",
       "      <td>1307.910651</td>\n",
       "      <td>1292.720876</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 14:00:21.448</th>\n",
       "      <td>1292.25</td>\n",
       "      <td>1292.75</td>\n",
       "      <td>1286.75</td>\n",
       "      <td>1286.75</td>\n",
       "      <td>54266</td>\n",
       "      <td>70000564.25</td>\n",
       "      <td>10157</td>\n",
       "      <td>1299.136791</td>\n",
       "      <td>1308.500955</td>\n",
       "      <td>1289.772626</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 14:01:38.747</th>\n",
       "      <td>1286.75</td>\n",
       "      <td>1287.00</td>\n",
       "      <td>1284.00</td>\n",
       "      <td>1284.50</td>\n",
       "      <td>54475</td>\n",
       "      <td>70034099.00</td>\n",
       "      <td>8778</td>\n",
       "      <td>1297.922642</td>\n",
       "      <td>1308.843635</td>\n",
       "      <td>1287.001649</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 14:03:22.782</th>\n",
       "      <td>1284.50</td>\n",
       "      <td>1285.00</td>\n",
       "      <td>1278.75</td>\n",
       "      <td>1279.00</td>\n",
       "      <td>54589</td>\n",
       "      <td>70000494.25</td>\n",
       "      <td>10823</td>\n",
       "      <td>1296.418749</td>\n",
       "      <td>1309.539297</td>\n",
       "      <td>1283.298202</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 14:04:31.604</th>\n",
       "      <td>1279.00</td>\n",
       "      <td>1281.50</td>\n",
       "      <td>1276.00</td>\n",
       "      <td>1276.00</td>\n",
       "      <td>55049</td>\n",
       "      <td>70397187.25</td>\n",
       "      <td>10651</td>\n",
       "      <td>1294.858757</td>\n",
       "      <td>1309.982621</td>\n",
       "      <td>1279.734893</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            open     high      low    close  cum_vol  \\\n",
       "date_time                                                              \n",
       "2011-08-01 13:56:56.220  1293.25  1294.00  1292.00  1292.25    54159   \n",
       "2011-08-01 14:00:21.448  1292.25  1292.75  1286.75  1286.75    54266   \n",
       "2011-08-01 14:01:38.747  1286.75  1287.00  1284.00  1284.50    54475   \n",
       "2011-08-01 14:03:22.782  1284.50  1285.00  1278.75  1279.00    54589   \n",
       "2011-08-01 14:04:31.604  1279.00  1281.50  1276.00  1276.00    55049   \n",
       "\n",
       "                          cum_dollar  cum_ticks          avg        upper  \\\n",
       "date_time                                                                   \n",
       "2011-08-01 13:56:56.220  70025279.50      12010  1300.315764  1307.910651   \n",
       "2011-08-01 14:00:21.448  70000564.25      10157  1299.136791  1308.500955   \n",
       "2011-08-01 14:01:38.747  70034099.00       8778  1297.922642  1308.843635   \n",
       "2011-08-01 14:03:22.782  70000494.25      10823  1296.418749  1309.539297   \n",
       "2011-08-01 14:04:31.604  70397187.25      10651  1294.858757  1309.982621   \n",
       "\n",
       "                               lower   rsi  side  \n",
       "date_time                                         \n",
       "2011-08-01 13:56:56.220  1292.720876  13.0   NaN  \n",
       "2011-08-01 14:00:21.448  1289.772626   9.0   1.0  \n",
       "2011-08-01 14:01:38.747  1287.001649   7.0   1.0  \n",
       "2011-08-01 14:03:22.782  1283.298202   5.0   1.0  \n",
       "2011-08-01 14:04:31.604  1279.734893   5.0   1.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log Returns\n",
    "raw_data['log_ret'] = np.log(raw_data['close']).diff()\n",
    "\n",
    "# Momentum\n",
    "raw_data['mom1'] = raw_data['close'].pct_change(periods=1)\n",
    "raw_data['mom2'] = raw_data['close'].pct_change(periods=2)\n",
    "raw_data['mom3'] = raw_data['close'].pct_change(periods=3)\n",
    "raw_data['mom4'] = raw_data['close'].pct_change(periods=4)\n",
    "raw_data['mom5'] = raw_data['close'].pct_change(periods=5)\n",
    "\n",
    "# Volatility\n",
    "window_stdev = 50\n",
    "raw_data['volatility'] = raw_data['log_ret'].rolling(window=window_stdev, min_periods=window_stdev, center=False).std()\n",
    "\n",
    "# Serial Correlation (Takes about 4 minutes)\n",
    "window_autocorr = 50\n",
    "\n",
    "raw_data['autocorr_1'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=1), raw=False)\n",
    "raw_data['autocorr_2'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=2), raw=False)\n",
    "raw_data['autocorr_3'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=3), raw=False)\n",
    "raw_data['autocorr_4'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=4), raw=False)\n",
    "raw_data['autocorr_5'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=5), raw=False)\n",
    "\n",
    "# Get the various log -t returns\n",
    "raw_data['log_t1'] = raw_data['log_ret'].shift(1)\n",
    "raw_data['log_t2'] = raw_data['log_ret'].shift(2)\n",
    "raw_data['log_t3'] = raw_data['log_ret'].shift(3)\n",
    "raw_data['log_t4'] = raw_data['log_ret'].shift(4)\n",
    "raw_data['log_t5'] = raw_data['log_ret'].shift(5)\n",
    "\n",
    "# Add fast and slow moving averages\n",
    "fast_window = 7\n",
    "slow_window = 15\n",
    "\n",
    "raw_data['fast_mavg'] = raw_data['close'].rolling(window=fast_window, min_periods=fast_window, center=False).mean()\n",
    "raw_data['slow_mavg'] = raw_data['close'].rolling(window=slow_window, min_periods=slow_window, center=False).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Trending signals\n",
    "raw_data['sma'] = np.nan\n",
    "\n",
    "long_signals = raw_data['fast_mavg'] >= raw_data['slow_mavg']\n",
    "short_signals = raw_data['fast_mavg'] < raw_data['slow_mavg']\n",
    "raw_data.loc[long_signals, 'sma'] = 1\n",
    "raw_data.loc[short_signals, 'sma'] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re compute sides\n",
    "raw_data['side'] = np.nan\n",
    "\n",
    "long_signals = raw_data['close'] <= raw_data['lower'] \n",
    "short_signals = raw_data['close'] >= raw_data['upper'] \n",
    "\n",
    "raw_data.loc[long_signals, 'side'] = 1\n",
    "raw_data.loc[short_signals, 'side'] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove look ahead bias\n",
    "raw_data = raw_data.shift(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now get the data at the specified events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rsi</th>\n",
       "      <th>side</th>\n",
       "      <th>log_ret</th>\n",
       "      <th>mom1</th>\n",
       "      <th>mom2</th>\n",
       "      <th>mom3</th>\n",
       "      <th>mom4</th>\n",
       "      <th>mom5</th>\n",
       "      <th>volatility</th>\n",
       "      <th>autocorr_1</th>\n",
       "      <th>autocorr_2</th>\n",
       "      <th>autocorr_3</th>\n",
       "      <th>autocorr_4</th>\n",
       "      <th>autocorr_5</th>\n",
       "      <th>log_t1</th>\n",
       "      <th>log_t2</th>\n",
       "      <th>log_t3</th>\n",
       "      <th>log_t4</th>\n",
       "      <th>log_t5</th>\n",
       "      <th>sma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08-02 17:02:16.995</th>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000990</td>\n",
       "      <td>-0.000990</td>\n",
       "      <td>-0.004339</td>\n",
       "      <td>-0.005516</td>\n",
       "      <td>-0.006299</td>\n",
       "      <td>-0.004732</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>-0.024846</td>\n",
       "      <td>0.030397</td>\n",
       "      <td>-0.055224</td>\n",
       "      <td>0.226567</td>\n",
       "      <td>0.122310</td>\n",
       "      <td>-0.003359</td>\n",
       "      <td>-0.001183</td>\n",
       "      <td>-0.000788</td>\n",
       "      <td>0.001576</td>\n",
       "      <td>-0.000591</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-02 18:06:11.084</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.002381</td>\n",
       "      <td>-0.002379</td>\n",
       "      <td>-0.002971</td>\n",
       "      <td>-0.005533</td>\n",
       "      <td>-0.005925</td>\n",
       "      <td>-0.005336</td>\n",
       "      <td>0.001762</td>\n",
       "      <td>-0.012131</td>\n",
       "      <td>0.013186</td>\n",
       "      <td>-0.193096</td>\n",
       "      <td>0.241242</td>\n",
       "      <td>0.083327</td>\n",
       "      <td>-0.000594</td>\n",
       "      <td>-0.002572</td>\n",
       "      <td>-0.000395</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.002374</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-02 19:45:56.176</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.002390</td>\n",
       "      <td>-0.002387</td>\n",
       "      <td>-0.002387</td>\n",
       "      <td>-0.003180</td>\n",
       "      <td>-0.005355</td>\n",
       "      <td>-0.007717</td>\n",
       "      <td>0.001723</td>\n",
       "      <td>0.026686</td>\n",
       "      <td>-0.181886</td>\n",
       "      <td>-0.124395</td>\n",
       "      <td>0.193267</td>\n",
       "      <td>-0.017387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000795</td>\n",
       "      <td>-0.002184</td>\n",
       "      <td>-0.002377</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-03 14:37:04.871</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.002624</td>\n",
       "      <td>-0.002620</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>-0.003424</td>\n",
       "      <td>-0.006625</td>\n",
       "      <td>-0.009013</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.034175</td>\n",
       "      <td>-0.134653</td>\n",
       "      <td>-0.011515</td>\n",
       "      <td>-0.083964</td>\n",
       "      <td>-0.118366</td>\n",
       "      <td>-0.002014</td>\n",
       "      <td>0.001208</td>\n",
       "      <td>-0.003217</td>\n",
       "      <td>-0.002406</td>\n",
       "      <td>-0.001601</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-03 14:44:34.209</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001014</td>\n",
       "      <td>-0.004244</td>\n",
       "      <td>-0.006853</td>\n",
       "      <td>-0.008851</td>\n",
       "      <td>0.001704</td>\n",
       "      <td>0.072997</td>\n",
       "      <td>-0.083414</td>\n",
       "      <td>-0.002388</td>\n",
       "      <td>-0.028741</td>\n",
       "      <td>-0.036896</td>\n",
       "      <td>-0.001014</td>\n",
       "      <td>-0.003239</td>\n",
       "      <td>-0.002624</td>\n",
       "      <td>-0.002014</td>\n",
       "      <td>0.001208</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          rsi  side   log_ret      mom1      mom2      mom3  \\\n",
       "2011-08-02 17:02:16.995  16.0   1.0 -0.000990 -0.000990 -0.004339 -0.005516   \n",
       "2011-08-02 18:06:11.084  19.0   1.0 -0.002381 -0.002379 -0.002971 -0.005533   \n",
       "2011-08-02 19:45:56.176  25.0   1.0 -0.002390 -0.002387 -0.002387 -0.003180   \n",
       "2011-08-03 14:37:04.871  24.0   1.0 -0.002624 -0.002620 -0.004627 -0.003424   \n",
       "2011-08-03 14:44:34.209  17.0   1.0  0.000000  0.000000 -0.001014 -0.004244   \n",
       "\n",
       "                             mom4      mom5  volatility  autocorr_1  \\\n",
       "2011-08-02 17:02:16.995 -0.006299 -0.004732    0.001709   -0.024846   \n",
       "2011-08-02 18:06:11.084 -0.005925 -0.005336    0.001762   -0.012131   \n",
       "2011-08-02 19:45:56.176 -0.005355 -0.007717    0.001723    0.026686   \n",
       "2011-08-03 14:37:04.871 -0.006625 -0.009013    0.001713    0.034175   \n",
       "2011-08-03 14:44:34.209 -0.006853 -0.008851    0.001704    0.072997   \n",
       "\n",
       "                         autocorr_2  autocorr_3  autocorr_4  autocorr_5  \\\n",
       "2011-08-02 17:02:16.995    0.030397   -0.055224    0.226567    0.122310   \n",
       "2011-08-02 18:06:11.084    0.013186   -0.193096    0.241242    0.083327   \n",
       "2011-08-02 19:45:56.176   -0.181886   -0.124395    0.193267   -0.017387   \n",
       "2011-08-03 14:37:04.871   -0.134653   -0.011515   -0.083964   -0.118366   \n",
       "2011-08-03 14:44:34.209   -0.083414   -0.002388   -0.028741   -0.036896   \n",
       "\n",
       "                           log_t1    log_t2    log_t3    log_t4    log_t5  sma  \n",
       "2011-08-02 17:02:16.995 -0.003359 -0.001183 -0.000788  0.001576 -0.000591 -1.0  \n",
       "2011-08-02 18:06:11.084 -0.000594 -0.002572 -0.000395  0.000593  0.002374 -1.0  \n",
       "2011-08-02 19:45:56.176  0.000000 -0.000795 -0.002184 -0.002377  0.001188 -1.0  \n",
       "2011-08-03 14:37:04.871 -0.002014  0.001208 -0.003217 -0.002406 -0.001601 -1.0  \n",
       "2011-08-03 14:44:34.209 -0.001014 -0.003239 -0.002624 -0.002014  0.001208 -1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get features at event dates\n",
    "X = raw_data.loc[labels.index, :]\n",
    "\n",
    "# Drop unwanted columns\n",
    "X.drop(['avg', 'upper', 'lower', 'open', 'high', 'low', 'close', 'cum_vol', 'cum_dollar', 'cum_ticks','fast_mavg', 'slow_mavg',], axis=1, inplace=True)\n",
    "\n",
    "y = labels['bin']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training, validation and test sets\n",
    "X_training_validation = X['2011-09-01':'2018-01-01']\n",
    "y_training_validation = y['2011-09-01':'2018-01-01']\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_training_validation, y_training_validation, test_size=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2385\n",
       "1     531\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.concat([y_train, X_train], axis=1, join='inner')\n",
    "train_df['bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2385\n",
       "0    2385\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upsample the training data to have a 50 - 50 split\n",
    "# https://elitedatascience.com/imbalanced-classes\n",
    "majority = train_df[train_df['bin'] == 0]\n",
    "minority = train_df[train_df['bin'] == 1]\n",
    "\n",
    "new_minority = resample(minority, \n",
    "                   replace=True,     # sample with replacement\n",
    "                   n_samples=majority.shape[0],    # to match majority class\n",
    "                   random_state=42)\n",
    "\n",
    "train_df = pd.concat([majority, new_minority])\n",
    "train_df = shuffle(train_df, random_state=42)\n",
    "\n",
    "train_df['bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training data\n",
    "y_train = train_df['bin']\n",
    "X_train= train_df.loc[:, train_df.columns != 'bin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'max_depth':[2, 3, 4, 5, 7],\n",
    "              'n_estimators':[1, 10, 25, 50, 100, 256, 512],\n",
    "              'random_state':[42]}\n",
    "    \n",
    "def perform_grid_search(X_data, y_data):\n",
    "    rf = RandomForestClassifier(criterion='entropy')\n",
    "    \n",
    "    clf = GridSearchCV(rf, parameters, cv=4, scoring='roc_auc', n_jobs=3)\n",
    "    \n",
    "    clf.fit(X_data, y_data)\n",
    "    \n",
    "    print(clf.cv_results_['mean_test_score'])\n",
    "    \n",
    "    return clf.best_params_['n_estimators'], clf.best_params_['max_depth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.55094958 0.63444028 0.64269547 0.64590126 0.65243522 0.6551371\n",
      " 0.65437732 0.57758233 0.67091872 0.68938581 0.69095754 0.69786364\n",
      " 0.70214677 0.70178009 0.59146097 0.70411721 0.73210807 0.74049967\n",
      " 0.74964398 0.7546167  0.75565736 0.6235073  0.75783106 0.78250248\n",
      " 0.7928879  0.80364894 0.80846502 0.81013727 0.6852779  0.84851194\n",
      " 0.87912388 0.88530658 0.89584192 0.89990841 0.90331725]\n",
      "512 7 42\n"
     ]
    }
   ],
   "source": [
    "# extract parameters\n",
    "n_estimator, depth = perform_grid_search(X_train, y_train)\n",
    "c_random_state = 42\n",
    "print(n_estimator, depth, c_random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=7, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=512, n_jobs=None,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Model\n",
    "rf = RandomForestClassifier(max_depth=depth, n_estimators=n_estimator,\n",
    "                            criterion='entropy', random_state=c_random_state)\n",
    "rf.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.79      0.86      2385\n",
      "           1       0.82      0.94      0.88      2385\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      4770\n",
      "   macro avg       0.88      0.87      0.87      4770\n",
      "weighted avg       0.88      0.87      0.87      4770\n",
      "\n",
      "Confusion Matrix\n",
      "[[1893  492]\n",
      " [ 137 2248]]\n",
      "\n",
      "Accuracy\n",
      "0.8681341719077568\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performance Metrics\n",
    "y_pred_rf = rf.predict_proba(X_train)[:, 1]\n",
    "y_pred = rf.predict(X_train)\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_train, y_pred_rf)\n",
    "print(classification_report(y_train, y_pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_train, y_pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_train, y_pred))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.93      0.87       578\n",
      "           1       0.39      0.16      0.23       151\n",
      "\n",
      "   micro avg       0.77      0.77      0.77       729\n",
      "   macro avg       0.60      0.55      0.55       729\n",
      "weighted avg       0.72      0.77      0.73       729\n",
      "\n",
      "Confusion Matrix\n",
      "[[540  38]\n",
      " [127  24]]\n",
      "\n",
      "Accuracy\n",
      "0.7736625514403292\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Meta-label\n",
    "# Performance Metrics\n",
    "y_pred_rf = rf.predict_proba(X_validate)[:, 1]\n",
    "y_pred = rf.predict(X_validate)\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_validate, y_pred_rf)\n",
    "print(classification_report(y_validate, y_pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_validate, y_pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_validate, y_pred))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       578\n",
      "           1       0.21      1.00      0.34       151\n",
      "\n",
      "   micro avg       0.21      0.21      0.21       729\n",
      "   macro avg       0.10      0.50      0.17       729\n",
      "weighted avg       0.04      0.21      0.07       729\n",
      "\n",
      "Confusion Matrix\n",
      "[[  0 578]\n",
      " [  0 151]]\n",
      "\n",
      "Accuracy\n",
      "0.20713305898491083\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Primary model\n",
    "primary_forecast = pd.DataFrame(labels['bin'])\n",
    "primary_forecast['pred'] = 1\n",
    "primary_forecast.columns = ['actual', 'pred']\n",
    "\n",
    "start = primary_forecast.index.get_loc('2016-08-01 10:31:44.455000')\n",
    "end = primary_forecast.index.get_loc('2017-12-29 21:03:37.018000') + 1\n",
    "\n",
    "subset_prim = primary_forecast[start:end]\n",
    "\n",
    "# Performance Metrics\n",
    "actual = subset_prim['actual']\n",
    "pred = subset_prim['pred']\n",
    "print(classification_report(y_true=actual, y_pred=pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(actual, pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(actual, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "title = 'Feature Importance:'\n",
    "figsize = (15, 5)\n",
    "\n",
    "feat_imp = pd.DataFrame({'Importance':rf.feature_importances_})    \n",
    "feat_imp['feature'] = X.columns\n",
    "feat_imp.sort_values(by='Importance', ascending=False, inplace=True)\n",
    "feat_imp = feat_imp\n",
    "\n",
    "feat_imp.sort_values(by='Importance', inplace=True)\n",
    "feat_imp = feat_imp.set_index('feature', drop=True)\n",
    "feat_imp.plot.barh(title=title, figsize=figsize)\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how low the side is ranked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Performance Tear Sheets (In-sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up the function to extract the KPIs from pyfolio\n",
    "perf_func = pf.timeseries.perf_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_daily_returns(intraday_returns):\n",
    "    \"\"\"\n",
    "    This changes returns into daily returns that will work using pyfolio. Its not perfect...\n",
    "    \"\"\"\n",
    "    \n",
    "    cum_rets = ((intraday_returns + 1).cumprod())\n",
    "\n",
    "    # Downsample to daily\n",
    "    daily_rets = cum_rets.resample('B').last()\n",
    "\n",
    "    # Forward fill, Percent Change, Drop NaN\n",
    "    daily_rets = daily_rets.ffill().pct_change().dropna()\n",
    "    \n",
    "    return daily_rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2016-08-02</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2017-12-29</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>17</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>146.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>275.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>67.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>10.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-14.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>14.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>247.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-8.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dates = X_validate.index\n",
    "\n",
    "base_rets = labels.loc[test_dates, 'ret']\n",
    "primary_model_rets = get_daily_returns(base_rets)\n",
    "\n",
    "# Save the statistics in a dataframe\n",
    "perf_stats_all = perf_func(returns=primary_model_rets, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "perf_stats_df = pd.DataFrame(data=perf_stats_all, columns=['Primary Model'])\n",
    "\n",
    "# pf.create_returns_tear_sheet(labels.loc[test_dates, 'ret'], benchmark_rets=None)\n",
    "pf.show_perf_stats(primary_model_rets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/empyrical/stats.py:1511: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  np.abs(np.percentile(returns, 5))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2016-08-02</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2017-12-29</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>17</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>27.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>42.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>11.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>12.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-2.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>12.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>10.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>147.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-1.4%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meta_returns = labels.loc[test_dates, 'ret'] * y_pred\n",
    "daily_meta_rets = get_daily_returns(meta_returns)\n",
    "\n",
    "# save the KPIs in a dataframe\n",
    "perf_stats_all = perf_func(returns=daily_meta_rets, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "\n",
    "perf_stats_df['Meta Model'] = perf_stats_all\n",
    "\n",
    "# pf.create_returns_tear_sheet(meta_returns, benchmark_rets=None)\n",
    "pf.show_perf_stats(daily_meta_rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Perform out-of-sample test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extarct data for out-of-sample (OOS)\n",
    "X_oos = X['2018-01-02':]\n",
    "y_oos = y['2018-01-02':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.68      0.75       749\n",
      "           1       0.20      0.41      0.27       151\n",
      "\n",
      "   micro avg       0.63      0.63      0.63       900\n",
      "   macro avg       0.53      0.54      0.51       900\n",
      "weighted avg       0.74      0.63      0.67       900\n",
      "\n",
      "Confusion Matrix\n",
      "[[506 243]\n",
      " [ 89  62]]\n",
      "\n",
      "Accuracy\n",
      "0.6311111111111111\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performance Metrics\n",
    "y_pred_rf = rf.predict_proba(X_oos)[:, 1]\n",
    "y_pred = rf.predict(X_oos)\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_oos, y_pred_rf)\n",
    "print(classification_report(y_oos, y_pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_oos, y_pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_oos, y_pred))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       749\n",
      "           1       0.17      1.00      0.29       151\n",
      "\n",
      "   micro avg       0.17      0.17      0.17       900\n",
      "   macro avg       0.08      0.50      0.14       900\n",
      "weighted avg       0.03      0.17      0.05       900\n",
      "\n",
      "Confusion Matrix\n",
      "[[  0 749]\n",
      " [  0 151]]\n",
      "\n",
      "Accuracy\n",
      "0.16777777777777778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Primary model\n",
    "primary_forecast = pd.DataFrame(labels['bin'])\n",
    "primary_forecast['pred'] = 1\n",
    "primary_forecast.columns = ['actual', 'pred']\n",
    "\n",
    "subset_prim = primary_forecast['2018-01-02':]\n",
    "\n",
    "# Performance Metrics\n",
    "actual = subset_prim['actual']\n",
    "pred = subset_prim['pred']\n",
    "print(classification_report(y_true=actual, y_pred=pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(actual, pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(actual, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Performance Tear Sheets (Out-of-sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2018-01-04</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2019-01-28</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>13</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>17.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>19.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>95.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-61.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>12.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-11.7%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dates = X_oos.index\n",
    "\n",
    "base_rets_oos = labels.loc[test_dates, 'ret']\n",
    "primary_model_rets_oos = get_daily_returns(base_rets_oos)\n",
    "\n",
    "# Save the statistics in a dataframe\n",
    "perf_stats_all = perf_func(returns=primary_model_rets_oos, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "\n",
    "perf_stats_df['Primary Model OOS'] = perf_stats_all\n",
    "\n",
    "\n",
    "# pf.create_returns_tear_sheet(labels.loc[test_dates, 'ret'], benchmark_rets=None)\n",
    "pf.show_perf_stats(primary_model_rets_oos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2018-01-04</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2019-01-28</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>13</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>35.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>39.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>56.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-36.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>-0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-7.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: \n",
      "The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'\n",
      "instead.\n",
      "The behavior of 'argmin' will be corrected to return the positional\n",
      "minimum in the future. For now, use 'series.values.argmin' or\n",
      "'np.argmin(np.array(values))' to get the position of the minimum\n",
      "row.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.78</td>\n",
       "      <td>2018-11-09</td>\n",
       "      <td>2018-12-19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.98</td>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>2018-05-08</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.94</td>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.87</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>2018-10-29</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.11</td>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>2018-06-27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: \n",
      "The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'\n",
      "instead.\n",
      "The behavior of 'argmin' will be corrected to return the positional\n",
      "minimum in the future. For now, use 'series.values.argmin' or\n",
      "'np.argmin(np.array(values))' to get the position of the minimum\n",
      "row.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x4752 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meta_returns = labels.loc[test_dates, 'ret'] * y_pred\n",
    "daily_rets_meta = get_daily_returns(meta_returns)\n",
    "\n",
    "# save the KPIs in a dataframe\n",
    "perf_stats_all = perf_func(returns=daily_rets_meta, \n",
    "                           factor_returns=None, \n",
    "                           positions=None,\n",
    "                           transactions=None,\n",
    "                           turnover_denom=\"AGB\")\n",
    "\n",
    "perf_stats_df['Meta Model OOS'] = perf_stats_all\n",
    "\n",
    "pf.create_returns_tear_sheet(daily_rets_meta, benchmark_rets=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Conclusion\n",
    "There are a few takeaways from the Bollinger bands mean-reverting strategy:\n",
    "1. In out-of-sample tests, the meta-model out-performs the primary model in all key statistics:\n",
    "    * Annualized returns: 17.7% vs. 35.3%\n",
    "    * Annualized volatility: 95% vs. 40%\n",
    "    * Sharpe ratio: 0.65 vs. 0.82 \n",
    "2. In all cases the meta-model (one with meta-labeling) decreases annualized volatility, maximum drawdown and increases Sharpe ratio compared to the primary model     \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
